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    <span>
      <i class="fa fa-calendar"></i>2019-09-28
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        | <a href="/blog/categories/CV/"><i class="fa fa-bookmark"></i>CV</a>
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  <h1 class="passage-title">
    Object Detection 简单整理
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    <h1 id="object-detection-任务"><a class="markdownIt-Anchor" href="#object-detection-任务"></a> Object Detection 任务</h1>
<p>输入：图片</p>
<p>输出：图片中各物体实例的类别和位置</p>
<p><img src="https://gluon-cv.mxnet.io/_static/object-detection.png" alt></p>
<h1 id="prerequest"><a class="markdownIt-Anchor" href="#prerequest"></a> Prerequest</h1>
<ul>
<li>深度学习基础</li>
</ul>
<h1 id="主流方法"><a class="markdownIt-Anchor" href="#主流方法"></a> 主流方法</h1>
<p>主流的 Object Detection 分为两大类：one-stage和two-stage。</p>
<p>One-stage方法，图片只经过一次神经网络就可以预测出各物体的类别和位置。特点是速度快，但准确率相对低，尤其不擅长预测小而密集的物体，对物体类别的判断较不准确。其代表有SSD, YOLOv3, RetinaNet。</p>
<p>Two-stage方法，图片需要经过两次神经网络，第一次得到物体的proposal，第二次得到最后的预测结果。特点是速度慢准确率高，主要代表为Faster R-CNN及其衍生版本。</p>
<p>上述说法并不绝对，例如one-stage的RetinaNet在MS COCO上的测试结果好于Faster R-CNN，而two-stage的Light-Head R-CNN速度可以超过one-stage。</p>
<h1 id="backbone"><a class="markdownIt-Anchor" href="#backbone"></a> Backbone</h1>
<ul>
<li>Resnet <a href="https://arxiv.org/abs/1512.03385" target="_blank" rel="noopener">arxiv</a></li>
<li>FPN <a href="https://arxiv.org/abs/1612.03144" target="_blank" rel="noopener">arxiv</a></li>
</ul>
<h1 id="fundataion"><a class="markdownIt-Anchor" href="#fundataion"></a> Fundataion</h1>
<h1 id="reference"><a class="markdownIt-Anchor" href="#reference"></a> Reference</h1>
<ul>
<li>J. Jordan, “An overview of object detection: one-stage methods,” Jeremy Jordan, 11-Jul-2018. [Online]. Available: <a href="https://www.jeremyjordan.me/object-detection-one-stage/" target="_blank" rel="noopener">https://www.jeremyjordan.me/object-detection-one-stage/</a>. [Accessed: 28-Sep-2019].<br>
‌</li>
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    <ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#object-detection-任务"><span class="toc-text"> Object Detection 任务</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#prerequest"><span class="toc-text"> Prerequest</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#主流方法"><span class="toc-text"> 主流方法</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#backbone"><span class="toc-text"> Backbone</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#fundataion"><span class="toc-text"> Fundataion</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#reference"><span class="toc-text"> Reference</span></a></li></ol>
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